ABCDEFGHIJKLMNOPQRSTUVWXYZAAAB
1
Name Surname of group membersGroup NoAlgorithmFunction 1Function 2Function 3Function 4Function 5Function 6Functin 7Function 8Function 9Function 10Function 11Function 12Function 13Function 14Function 15Function 16Function 17Function 18Function 19Function 20Function 21Function 22Function 23Function 24
2
Global minimum21.126.91311.6311.6-48.47-91.3632.4971.6-356.751.03-96.65553.399.88405.4764.25-43.28227.51227.5173.06-123.81-44.42222.1-1000-1.33
3
Matej Pičulin1
GenSA - for demonstration purpuses only, you are not allowed to use already implemented algorithms
21.126.91311.6311.6-48.47-91.3676.3942171.6-356.751.0719-96.64997553.399.880672405.47388.6039-32.68815230.9546242.044276.42571-123.129-42.48226224.6905-999.4498427.5631
4
1
Random search - 10000 iterations
21.2666345324.37980.1141919.4602-48.47-49.93835546.4757125.077-303.007737144.89182.4922113476.193.43493405.56351150.376-24.91597238.039267.860279.37629-120.5242-43.17837223.2854-996.754481.6922
5
Aljaž Šuštar, Simon Goričar2Simulated annealing21.142.0107322.8852328.2774-48.4032-90.0121105.685375.7075-323.31525483.2111154.1548562.610710.9134405.5071279.1458-35.7465231.4089248.410977.9123-123.1702-43.2329224.8712-999.07959203.3282
6
2Firefly optimization21.139.521366827.9766862.361468-41.966896-91.35996843.77727571.612753-356.2297162.850576-96.649999553.60867111.129805405.470091174.720834-37.357435234.303582253.53107174.819706-121.641126-43.725893224.690548-999.72324180.865046
7
Nika Molan, Nina Mislej3
Gradient Descent - 300 iterations
21.139815.631441.911747.75-23.91-29.922225.85499.89-120.22139871.12341.97728246.51288.43405.49575.29-9.14229.34253.2890.73-122.66-43.72224.16-995.72840.97
8
3
Simulated annealing - 3000 iterations
21.3925920.631256.071330.9168.67-67.81433.45122.27-310.4226798.82160.0276399.5474.85405.691246.38-31.87238262.2691.88-121.81-44.3224.12-997.651041.31
9
3
Covariance Matrix Adaptation Evolution Strategy - 10000 iterations (overall best)
21.111063.63445.92694.65-40.03-90.4599.46100.35-349.911058.8732.13553.479.9405.48206.53-34.93229.88232.0973.99-122.25-38.17224.69-998.5658.71
10
3
Nelder-Mead Method - 1000000 iterations
21.121727.951157.3846.91-48.4792.68189.3145.33-324.232583.39-16.14554.7618.3405.481169.91-17.1241.31281.0473.08-121.28-38.17224.69-997.62240.05
11
Darko Bashukoski4
Particle Swarm Optimization
21.13247.2021662.8173886.67819-48.47-15.61105.39105.7092-280.141777.91-59.44553.410.2405.47259.26-28.151233.6243.0979.83-121.6-43.72229.37-998.17195.49
12
4
Best Descent Local Search
21.1449.183909.815267.33-48.47-91.364354.9971.91-356.44324.1-96.65561.213.4405.47470719.56244.28330.16113.23-122.46-42.48229.37-997.741124.48
13
Žiga Leskovec, Maksimiljan Vojvoda5Differential evolution23.0491027.114653.214706.883-34.219238.13781.295479.218368.769601743.668281.0191662482.712236.442408.835459.697-10.923230.538238.98981.132-120.312-37.667229.873-996.264422.581
14
5Simulated annealing21.1195571.229440.983505.74-44.778-91.27845.985103.409-334.8728410.70989.045553.399.88405.557182.734-39.132229.892237.48177.069-122.544-43.912298.771-999.814217.667
15
Nikola Simjanovski, Kristijan Pucoski6Harmonic Search21.11190275104.4818656317.5766447321.7807405-44.92858468-76.3170296369.80221377213.418237-317.096247779559.2585291.9299113611085.5132443.45336972405.5094416300.9541768-9.641255098228.483514229.544568379.35828714-123.1055304-38.16499935224.7121453-996.3973826326.0386066
16
6Guided Local Search21.2935368514422.98232850.83095931143.698315-48.47-87.0421447853.05111778125.3648932-300.92593419761.39452-96.08789321165055.3207100.4374746405.5817981616.6337221-35.37667845232.3032863250.387721582.68231035-121.3019791-44.41900683229.4278816-997.8389013631.0925288
17
Veronika Matek, Lenart Arvo Kos7Simulated annealing21.1892426404.531412.5371526.42-48.47-89.69414109.4635118.7606-310.448618424.46-86.2778273052.3663.97484405.52741318.613-31.26234242.9315288.896591.86351-121.6169-42.62867224.0758-996.82781073.277
18
7Differential evolution21.10408175.1407368.3486381.5675-48.47-31.6204542.17059259.3349-318.2901133836.6153.435131559.7108.2369405.5074344.1326-12.04215227.5355228.332879.54168-121.6065-40.09101224.71-996.8182330.0722
19
Matej Klančar, Anže Hočevar8Tabu Search21.100515855.3387964.85231138.3855159.6022-51.1477200.2986103.574-323.799614322.5101244.67973.960517.1009405.473525.3795-40.6193253.6361360.097284.014-122.1933-31.5651243.8798-999.4627654.0865
20
8Genetic algorithm21.293614063.0087335.6546364.2041-39.305426.449647.0885243.2846-288.7976658253.8999220.6028309143.9476150.4163405.6382325.824-20.5595228.0657229.298979.319-121.6298-38.0497224.8246-996.6262280.8592
21
8Differential evolution21.126.91338.5531354.3832-48.4689-91.256134.65289.5849-327.16297503.893215.5838553.599412.6526405.4709348.3936-14.4539227.516227.570979.8347-123.0303-38.1707224.6905-996.8197342.1307
22
Aljaž Knific, Marko Zupančič Muc9Tabu search21.284989265.232495.2873545.0342-48.47-85.1983435.71914123.3908-302.1228114.32125.63053160385.884.91568405.5731264.4745-39.81703238.6292273.901279.54796-122.0556-34.40253229.434-996.0247393.1687
23
9Differential evolution21.1000426.94861374.2688386.57-48.47-38.8818440.43224106.6557-328.002729621.26-17.47066938.692622.20397405.5713307.1473-16.83623227.7115229.600779.59201-122.1567-42.4819224.6912-997.1813335.7155
24
Jan Anžur10Simulated annealing21.116887.64251628.010172541.466465-22.586309-91.35785582.518437107.246385-321.9054479068.46498930.275396553.40995412.850437405.471424193.676659-33.605853230.646499239.45581881.97927-122.077704-38.170684224.690548-999.331745423.764672
25
10Differential evolution21.10000126.92622355.380973525.518584-48.423925-91.35793935.463773106.202652-318.6757937769.11265-24.858165554.03481311.508951405.471373136.890485-35.013253227.543363228.53090575.329304-121.890205-38.170683224.690549-999.64382363.822224
26
Vid Purgar, Vid Smole11Best descent21.192993818794.055531733.5225712048.290736-48.47-88.9397128361.74643191168.8333668-310.344060219805.04981-94.5880397258633.4367959.36352066405.52348442057.471585-24.78454416261.5397067324.995206298.39354536-121.385321821.42403366252.240204-996.46821311646.157635
27
11
Particle Swarm Optimization
21.11200.734371435.1691195526.0677181-48.4712.1147969280.76678249106.7688441-317.76068326876.033551-87.26493105553.578278711.52829774405.4710079237.372896-28.04722799230.4342221237.80648675.24306774-122.082243-38.17068498224.6905487-997.6019364146.2986414
28
11Differential evolution21.1026.91311.60311.60-48.47-49.10115.91102.15-322.65104505.62114.491045.0514.54405.48376.17-31.42232.92246.3779.46-123.36-44.08223.78-998.59378.41
29
11Simulated Annealing21.10646.453044.515248.08-48.47-91.367140.3171.86-356.49543.49-96.65562.0811.35405.472456.9212.06247.42374.64137.87-121.90-38.64224.69-998.351074.67
30
Ole Lenz, Nojus Gudinavičius12
Particle Swarm optimization
21.2570570214.46814.8423837.6887143.7741-79.34754248.8927153.7905-299.584452074.7560.3333172289.992.38464405.5996473.7888-29.14313232.7912248.220176.0871-120.6848-42.74475224.7947-998.6726271.6695
31
12SA21.488051282030617.9761572.2763711590.03623198.38016594-8.111783646962.3467552134.5568702-260.632184835788.79561232.88778854925615.524238.8117308407.70479331597.416666-32.41572854240.9865892269.083817891.76915726869.9549071-41.01125143231.0440658-995.9246712976.3829657
32
Tadej Logar13Simulated annealing21.4859108739.82811366.33671353.1913-10.6504-63.9419123.3868147.2426-280.953248587.0915231.5436235522.737145.628405.68561170.3169-27.8733244.6514356.070888.3851-121.1305-38.6653229.5097-995.61331558.5303
33
13Particle Swarm26.708427688.0449465.5393543.72534.8356417.2005109.8513571.9974-267.66726410.748240.70011624451.047267.0228405.6754303.7988-34.6021230.9063232.948980.028-122.3954-35.4528228.7421-996.7374314.7438
34
Karim El Habashy14Simulated annealing21.21632557.8762179.1081984.499-9.142-81.033376.175186.206-191.60724669.049124.422107240.33572.962405.5692105.79313.905240.211323.18103.412-120.852-26.529243.949-994.9721858.435
35
14Genetic algorithm94.1511262093.172995.91387.139201.67463185.914629.76272367.96481908.0661526570.834339.179113576361.41580.753432.08857.589-7.693235.122256.58786.59517443.67424.021282.232-995.553695.435
36
Dustin Heither, Rok Filipovič15Artificial Bee Colony21.3859548592664.8009772.8475-48.47147.813130.054221.057-254.860325140.98-23.50971238157.9181.2332405.8058346.031-30.13906232.9156246.466276.24121-121.0472-41.99216225.1818-998.1748255.1919
37
15Genetic Algorithm756.2604634569898398.80728944.73874.161929783269495.41720567471323406603602244047623770268666125531.311940.20049159.32160.02888394.7131860.8758168.816630652.742.10598308.6584-979.77362007.467
38
15Simulated annealing27.77823258130451.0316650.1379208719.3799902-48.47-0.9487988877.329143241027.895853576.4308337121424.6464-54.90289475983391.865491.346513407.9072615421.5896936-13.61636942230.142633236.6204980.21775873-120.3026436-43.25963275228.9911614-996.504571352.1143561
39
15Tabu Search21.8823111922743.22169571.7968107614.9244486-42.00964718-72.2058638342.56031652185.7008874-224.550278417730.82583-56.92688852636641.246180.5845145405.8024483336.2207827-37.13671894228.377834231.473093779.30745276-121.072078-42.44323158224.1839254-997.5702892328.1946466
40
Niko Lemke16Simulated annealing21.2376824913609.06821988.0093802943.75317512.173729851-87.5642954150.11119194122.289761-308.083985710416.89808180.6368365116158.366475.85083922405.6323258711.3498724-39.5062275238.6217566284.454815484.80340547-121.927599-38.01548869225.0333793-997.32977841014.249226
41
16
Variable neighborhood search
21.10000001611.4777539448.9039108512.5811331-35.20533022-91.359998545.6935319290.98777372-335.9868776667.1685436-96.64999928553.435184410.07470485405.4700992224.4379049-40.62885823228.9240812233.544335375.44999766-122.3091006-42.4822595224.6905477-999.8143841187.7922919
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100